Online chat communication analysis via mono-recording system and methods

ABSTRACT

The methods, apparatus, non-transitory computer readable media, and systems described herein include recording a mono recording of a software and a customer inquiry communication using a microphone to interpret and respond to the customer inquiry communication, wherein the mono recording is unseparated and includes customer voice data and audio data generated by the software agent, separately and concurrently recording the software agent audio data in an agent recording, and subtracting agent audio data from the unseparated mono recording to provide a separated recording including only customer voice data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/389,780, filed Dec. 23, 2016, now allowed, which is a continuation ofU.S. patent application Ser. No. 15/046,635, filed Feb. 18, 2016, nowU.S. Pat. No. 9,538,008 B1, which is a continuation of U.S. patentapplication Ser. No. 14/610,136, filed Jan. 30, 2015, now U.S. Pat. No.9,300,801 B1, the entire contents of each which is hereby incorporatedherein by express reference thereto.

TECHNICAL FIELD

The present disclosure generally relates to methods, apparatus,non-transitory computer readable media, and systems that separatesounds, and more specifically separate voice data of a customer from amono recording of the customer and an agent based on onlinecommunication.

BACKGROUND OF THE DISCLOSURE

Large organizations, such as commercial organizations, financialinstitutions, government agencies or public safety organizations conductnumerous interactions with customers, users, suppliers and the like on adaily basis. Many of these interactions are vocal, or at least comprisea vocal or audio component, for example, voices of parties to a phonecall or the audio portion of a video or face-to-face interaction. Asignificant part of these interactions takes place between a customerand a representative of the organization, e.g., an agent in a contactcenter.

Contact centers typically do not record agent and customer portions oftheir calls in stereo or in separated channel audio, as telephonenetworks are typically mono. Without the separated data, it is notpossible to quickly and/or accurately perform an analysis on the callerand/or agent. Accordingly, improved methods and systems are needed andare disclosed herein that effectively separate customer voice data fromagent voice data so that the data can be separately analyzed.

SUMMARY

The present disclosure describes methods and systems that analyzeseparate voice data in customer/agent communications. In a first aspectof this embodiment, the disclosure encompasses a system for interpretingcustomer inquiry communications and responding that includes a nodecomprising a processor and a non-transitory computer readable mediumoperably coupled thereto, the non-transitory computer readable mediumcomprising a plurality of instructions stored in association therewiththat are accessible to, and executable by, the processor, wherein theplurality of instructions that, when executed: record, using amicrophone, a mono recording of a customer inquiry communicationreceived by a software agent comprising a software-based application,wherein the mono recording is unseparated and includes customer voicedata and audio data generated by the software agent; separately andconcurrently record the software agent audio data in an agent recording;and subtract the software agent audio data from the unseparated monorecording to provide a separated recording including only customer voicedata.

In a second aspect of this embodiment, the disclosure encompasses amethod for interpreting customer inquiry communications and responding,which includes: recording, using a microphone, a mono recording of acustomer inquiry communication received by a software agent comprising asoftware-based application, wherein the mono recording is unseparatedand includes customer voice data and audio data generated by thesoftware agent; separately and concurrently recording the software agentaudio data in an agent recording; and subtracting agent audio data fromthe unseparated mono recording to provide a separated recordingincluding only customer voice data.

In a third aspect, the disclosure encompasses a non-transitory computerreadable medium including a plurality of instructions, which in responseto a computer system, cause the computer system to perform a method thatincludes: recording, using a microphone, a mono recording of a customerinquiry communication received by a software agent comprising asoftware-based application, wherein the mono recording is unseparatedand includes customer voice data and audio data generated by thesoftware agent; separately and concurrently recording the software agentaudio data in an agent recording; and subtracting agent audio data fromthe unseparated mono recording to provide a separated recordingincluding only customer voice data. In each of the above aspects, onepreferred embodiment further includes instructions that, when executed,align the unseparated mono recording and the agent recording so they aretime-synched, wherein the instructions that, when executed, subtractagent audio data from the unseparated mono recording are based on theagent audio data being subtracted from the unseparated mono recordingbased on the alignment, sound frequency analysis, or both.

In another embodiment, the interaction is face-to-face between customerand agent. In one aspect of this embodiment, the present discloseencompasses a system for analyzing a face-to-face customer-agentcommunication that includes a node comprising a processor and anon-transitory computer readable medium operably coupled thereto, thenon-transitory computer readable medium comprising a plurality ofinstructions stored in association therewith that are accessible to, andexecutable by, the processor, wherein the plurality of instructions whenexecuted: record a mono recording of a communication between an agentand a customer using a microphone, wherein the mono recording isunseparated and includes agent voice data and customer voice data,separately record the agent voice data in an agent recording using asecond microphone; align the unseparated mono recording and the agentrecording so they are time-synched; subtract agent voice data from theunseparated mono recording using the agent recording to provide aseparated recording including only customer voice data, wherein theagent voice data is subtracted from the unseparated mono recording basedon the alignment, sound frequency analysis, or both; convert at leastthe customer voice data to text; and determine a personality type of thecustomer by applying one or more computer-implemented linguisticalgorithms to the text of the customer voice data.

In a second aspect, the disclosure relates to a method for analyzing aface-to-face customer-agent communication that includes recording a monorecording of a communication between an agent and a customer using amicrophone, wherein the mono recording is unseparated and includes agentvoice data and customer voice data; separately recording the agent voicedata in an agent recording using a second microphone; aligning theunseparated mono recording and the agent recording so they aretime-synched; subtracting agent voice data from the unseparated monorecording using the agent recording to provide a separated recordingincluding only customer voice data, wherein the agent voice data issubtracted from the unseparated mono recording based on the alignment,sound frequency analysis, or both; converting at least the customervoice data to text; and determining a personality type of the customerby applying one or more computer-implemented linguistic algorithms tothe text of the customer voice data.

In a third aspect, the disclosure relates to a non-transitory computerreadable medium comprising a plurality of instructions, which inresponse to a computer system, cause the computer system to perform amethod that includes recording a mono recording of a communicationbetween an agent and a customer using a microphone, wherein the monorecording is unseparated and includes agent voice data and customervoice data; separately recording the agent voice data in an agentrecording using a second microphone, aligning the unseparated monorecording and the agent recording so they are time-synched; subtractingagent voice data from the unseparated mono recording using the agentrecording to provide a separated recording including only customer voicedata, wherein the agent voice data is subtracted from the unseparatedmono recording based on the alignment, sound frequency analysis, orboth; converting at least the customer voice data to text; anddetermining a personality type of the customer by applying one or morecomputer-implemented linguistic algorithms to the text of the customervoice data.

In various embodiments applicable at least to the aspects above, thesystem, methods, and apparatus herein further apply voice printing tothe customer voice data to facilitate identification of the customer, orinstructions that, when executed, do the same. In another embodiment,the voice printing identifies the customer. In yet another embodiment,the agent is associated with one or more commercial organizations,financial institutions, government agencies or public safetyorganizations.

In a fourth aspect, the disclosure relates to a system for analyzing acustomer-agent communication, including a node including a processor anda non-transitory computer readable medium operably coupled thereto, thenon-transitory computer readable medium including a plurality ofinstructions stored in association therewith that are accessible to, andexecutable by, the processor, wherein the plurality of instructionsthat, when executed, record a mono recording of a communication betweenan agent and a customer, wherein the mono recording is unseparated andincludes agent voice data and customer voice data; separately record theagent voice data in an agent recording; align the unseparated monorecording and the agent recording so they are time-synched; subtractagent voice data from the mono recording using the agent recording toprovide a separated recording including only customer voice data,wherein the agent voice data is subtracted from the unseparated monorecording based on the alignment, sound frequency analysis, or both; andapply distress analysis to a portion of the communication to identifyone or more distress events.

In a fifth aspect, the disclosure relates to a methods for analyzing acustomer-agent communications, which includes recording, by one or moreprocessors, customer voice data and agent voice data in a communicationbetween an agent and a customer as an unseparated mono recording;separately and concurrently recording, by one or more processors, theagent voice data in an agent recording; subtracting, by one or moreprocessors, the agent voice data from the mono recording based on theagent recording and creating a separated customer recording includingonly customer voice data, wherein the agent voice data is subtractedfrom the unseparated mono recording based on alignment of theunseparated mono recording and the agent recording, sound frequencyanalysis, or both; and applying distress analysis to a portion of thecommunication to identify one or more distress events.

In a sixth aspect, the disclosure relates to a non-transitory computerreadable medium including a plurality of instructions, which in responseto a computer system, cause the computer system to perform a methodincluding recording a customer-agent communication between an agent anda customer as a mono recording, wherein the mono recording isunseparated and includes the agent voice and the customer voice;separately recording an agent voice in an agent recording; convertingthe mono recording to text; converting the agent recording to text;subtracting the text of the agent recording from the text of the monorecording so that only text of the customer voice remains, wherein theagent voice data is subtracted from the unseparated mono recording basedon alignment of the unseparated mono recording and the agent recording,sound frequency analysis, or both; and applying distress analysis to aportion of the communication to identify one or more distress events.

In one embodiment applicable at least to these above aspects, the systemfurther include instructions that, when executed, convert the monorecording to text. In a preferred embodiment, the system furtherincludes instructions that, when executed, convert the agent recordingto text. In a preferred embodiment, the system further includesinstructions that, when executed, subtract the text of the agentrecording from the text of the mono recording. In yet another preferredembodiment, the system further includes applying a computer implementedlinguistic algorithm to the text of the agent recording or the text ofthe separated customer recording. In a further preferred embodiment, thesystem further includes instructions that, when executed, evaluate theagent, provide training to the agent, or both, based on the distressevents identified in the communication.

In another embodiment, the system further includes a computerimplemented non-linguistic distress analytic tool applied to theseparated recording. In another embodiment, the system further includesapplying a computer-implemented linguistic distress analytic tool to thetext. In yet another embodiment, the system further includesinstructions that, when executed, generate and display on an agentdevice actionable tasks for the agent based on the identified distressevents. In a preferred embodiment, the actionable tasks include specificwords or actions. In another embodiment, the system further includesdetermining a personality type of the customer based on thecomputer-implemented linguistic algorithm applied to the text of theseparated recording including only customer voice data. In yet a furtherembodiment, the system further includes determining a personality typeof the agent based on the computer-implemented linguistic algorithmapplied to the text of the agent recording.

In another set of embodiments, the present methods evaluate customervoice data to determine personality type of the customer. The customerpersonality type can then be used to facilitate improved customerinteractions.

In one such aspect, the present disclosure relates to a system foranalyzing a customer communication. The system includes a node thatincludes a processor and a non-transitory computer readable mediumoperably coupled thereto, and the non-transitory computer readablemedium includes a plurality of instructions stored in associationtherewith that are accessible to, and executable by, the processor. Theplurality of instructions, when executed, record a mono recording of acommunication between an agent and a customer, wherein the monorecording includes agent voice data and customer voice data; separatelyrecord the agent voice data in an agent recording; subtract agent voicedata from the mono recording using the agent recording to provide aseparated recording including only customer voice data; convert at leastthe customer voice data to text; and determine a personality type of thecustomer by applying one or more computer-implemented linguisticalgorithms to the text of the customer voice data.

In a second such aspect, the present disclosure relates to a method foranalyzing customer communications. The method includes recording, by oneor more processors, customer voice data and agent voice data in acommunication between a customer and an agent as a mono recording;separately and concurrently recording, by one or more processors, theagent voice data in an agent recording; subtracting, by one or moreprocessors, the agent voice data from the mono recording based on theagent recording and creating a separated customer recording includingonly customer voice data; converting, by one or more processors, thecustomer voice data to text; and performing, by one or more processors,linguistic analysis on the text of the customer voice data to determinea personality type of the customer.

In a third such aspect, the present disclosure relates to anon-transitory computer readable medium that includes a plurality ofinstructions, which in response to a computer system, cause the computersystem to perform a method. The method includes recording a customercommunication between an agent and a customer as a mono recording,wherein the mono recording includes the agent voice and the customervoice; separately recording an agent voice in an agent recording;converting the mono recording to text; converting the agent recording totext; subtracting the text of the agent recording from the text of themono recording so that only text of a customer voice remains; andapplying a computer-implemented linguistic algorithm to the text of thecustomer voice to determine a personality type of the customer.

In another aspect, the disclosure encompasses a system for analyzing acustomer communication that includes a node including a processor and anon-transitory computer readable medium operably coupled thereto, thenon-transitory computer readable medium including a plurality ofinstructions stored in association therewith that are accessible to, andexecutable by, the processor, wherein the plurality of instructions whenexecuted: record a mono recording of a communication between an agentand a customer, wherein the mono recording includes agent voice data andcustomer voice data; subtract agent voice data from the mono recordingusing the agent recording to provide a separated recording includingonly customer voice data; convert at least the customer voice data totext; and determine a personality type of the customer by applying oneor more computer-implemented linguistic algorithms to the text of thecustomer voice data. Methods of analyzing customer/agent communicationsare also encompassed, such as by recording a mono recording of acommunication between an agent and a customer, wherein the monorecording includes agent voice data and customer voice data, analyzingthe recording to identify agent voice data and customer voice data inthe recording, subtracting agent voice data from the mono recordingusing the identified agent voice data to provide a separated recordingincluding only customer voice data; converting at least the customervoice data to text; and determining a personality type of the customerby applying one or more computer-implemented linguistic algorithms tothe text of the customer voice data. Various preferred embodimentsdisclosed herein are applicable to each of the above-noted aspects ofthe present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a block diagram of an embodiment of a system for analyzing acustomer communication according to various aspects of the presentdisclosure.

FIG. 2 is a detailed block diagram of the contact center of FIG. 1according to aspects of the present disclosure.

FIG. 3 is a flowchart illustrating a preferred method of analyzing acustomer communication according to aspects of the present disclosure.

FIG. 4 is a flowchart illustrating another preferred method of analyzinga customer communication according to aspects of the present disclosure.

FIG. 5 is a block diagram of a computer system suitable for implementingone or more components in FIG. 1 according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure advantageously describes methods and systems thatseparate customer and agent voice data in a communication so that one orboth can be analyzed. Speaker separation can provide valuable contextfor the content of customer interactions. Analysis of the separate voicedata can yield useful information related to customer satisfaction,agent performance, and business opportunities. In several embodiments,analysis of the words spoken by the customer provides the personalitytype of the customer, which can be used to guide an agent in thecommunication or even route or transfer a customer call to acomplementary agent able to handle (or best able to handle) thecommunication with customers of that personality type.

The methods disclosed herein include recording a customer-agentcommunication as a mono recording; separately recording a voice of anagent in an agent mono recording; subtracting agent mono voice data fromthe customer-agent mono recording so that only customer voice dataremains (either in the mono recording or in a newly created separatedrecording); converting the customer voice data to text; and performinglinguistic analysis on the text of the customer voice data to determinea personality type of the customer. In other embodiments, no separatingoccurs and the systems and methods are configured to analyze two singlechannel audio streams to isolate agent and customer audio data.

Systems and apparatuses for carrying out these methods are also part ofthe present disclosure. An exemplary system to analyze a customer-agentcommunication includes, for example, a node including a processor and acomputer readable medium operably coupled thereto, the computer readablemedium including a plurality of instructions stored in associationtherewith that are accessible to, and executable by, the processor. Inone embodiment, when executed, the plurality of instructions records amono recording of a communication between an agent and a customer andincludes agent voice data and customer voice data, separately recordsthe agent voice data in an agent recording, subtracts agent voice datafrom the mono recording to provide a separated recording including onlycustomer voice data (again, either on the mono recording or on a new,separated recording), convert at least the customer voice data to text,and determine a personality type of the customer by applying one or morecomputer-implemented linguistic algorithms to the text of the customervoice data. In various embodiments, the agent recording is an agent monorecording including agent mono voice data.

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It is nevertheless understood that no limitation tothe scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, and methods, and anyfurther application of the principles of the present disclosure arefully contemplated and included within the present disclosure as wouldnormally occur to one of ordinary skill in the art to which thedisclosure relates. In particular, it is fully contemplated that thefeatures, components, and/or steps described with respect to oneembodiment may be combined with the features, components, and/or stepsdescribed with respect to other embodiments of the present disclosure.For the sake of brevity, however, the numerous iterations of thesecombinations will not be described separately.

FIG. 1 is a simplified block diagram of an embodiment of a contactcenter 100 according to various aspects of the present disclosure. A“contact center” as used herein can include any facility or systemserver suitable for receiving and recording electronic communicationsfrom customers. Such customer-agent communications can include, forexample, telephone calls, video interactions, facsimile transmissions,e-mails, web interactions, and voice over IP (“VoIP”). Various specifictypes of communications contemplated through one or more of thesechannels include, without limitation, email, SMS data (e.g., text),tweet, instant message, web-form submission, smartphone app, socialmedia data, and web content data (including but not limited to internetsurvey data, blog data, microblog data, discussion forum data, and chatdata), etc. In some embodiments, the communications can include customertasks, such as taking an order, making a sale, responding to acomplaint, etc. It is contemplated that these communications may betransmitted by and through any type of telecommunication device and overany medium suitable for carrying such data. For example, thecommunications may be transmitted by or through telephone lines, cable,or wireless communications (e.g., satellite, Wi-Fi, Bluetooth, cellularphone network, etc.). As shown in FIG. 1, the contact center 100 of thepresent disclosure is adapted to receive and record varying electroniccommunications and data formats that represent an interaction that mayoccur between a customer (or caller) and a contact center agent duringfulfillment of a customer and agent transaction. In one embodiment, thecontact center 100 records all of the customer contacts in uncompressedaudio formats. In the illustrated embodiment, customers may communicatewith agents associated with the contact center 100 via multipledifferent communication networks, such as a public switched telephonenetwork (PSTN) 102 or the Internet 104, e.g., including via Skype®,Facetime®, Tango™, or any other communication app, program, website, orother software or hardware. For example, a customer may initiate aninteraction session through traditional telephones 106, a fax machine108, a cellular (i.e., mobile) telephone 110 (e.g., such as a smartphonewith video capabilities), a personal computing device 112 with a modem,or other legacy communication device via the PSTN 102. Further, thecontact center 100 may accept internet-based interaction sessions frompersonal computing devices 112, VoIP telephones 114, andinternet-enabled smartphones 116 and personal digital assistants (PDAs).

As one of ordinary skill in the art would recognize, the illustratedexample of communication channels associated with a contact center 100in FIG. 1 is just an example, and the contact center may accept customerinteractions, and other analyzed interaction information from ananalytics center, through various additional and/or different devicesand communication channels whether or not expressly described herein.

For example, in some embodiments, internet-based interactions,video-based interactions and/or telephone-based interactions may berouted through an analytics center 120 before reaching the contactcenter 100 or may be routed simultaneously to the contact center and theanalytics center (or even directly and only to the contact center). Insome instances, the analytics center 120 is a third-party analyticscompany that captures interaction data associated with the contactcenter 100 and applies computer-implemented linguistic algorithms to thedata to generate personality type data for the contact center. Forexample, the analytics center 120 may provide an analysis of customerand/or agent voice data according to the present disclosure, receive acustomer-agent communication, separate agent voice data from customervoice data, determine personality type of a customer, etc. The analyticscenter 120 may be controlled by the same entity or a different entitythan the contact center 100. Further, the analytics center 120 may be apart of, or independent of, the contact center 100.

FIG. 2 is a more detailed block diagram of an embodiment of the contactcenter 100 according to aspects of the present disclosure. As shown inFIG. 2, the contact center 100 is communicatively coupled to the PSTN102 via a distributed private branch exchange (PBX) switch 130. The PBXswitch 130 provides an interface between the PSTN 102 and a local areanetwork (LAN) 132 within the contact center 100. In general, the PBXswitch 130 connects trunk and line station interfaces of the PSTN 102 tocomponents communicatively coupled to the LAN 132. The PBX switch 130may be implemented with hardware or virtually. A hardware-based PBX maybe implemented in equipment located local to the user of the PBX system.In contrast, a virtual PBX may be implemented in equipment located at acentral telephone service provider that delivers PBX functionality as aservice over the PSTN 102. Additionally, in one embodiment, the PBXswitch 130 may be controlled by software stored on a telephony server134 coupled to the PBX switch. In another embodiment, the PBX switch 130may be integrated within telephony server 134. The telephony server 134incorporates PBX control software to control the initiation andtermination of connections between telephones within the contact center100 and outside trunk connections to the PSTN 102. In addition, thesoftware may monitor the status of all telephone stations coupled to theLAN 132 and may be capable of responding to telephony events to providetraditional telephone service. In certain embodiments, this may includethe control and generation of the conventional signaling tones includingwithout limitation dial tones, busy tones, ring back tones, as well asthe connection and termination of media streams between telephones onthe LAN 132. Further, the PBX control software may programmaticallyimplement standard PBX functions such as the initiation and terminationof telephone calls, either across the network or to outside trunk lines,the ability to put calls on hold, to transfer, park and pick up calls,to conference multiple callers, and to provide caller ID information.Telephony applications such as voice mail and auto attendant may beimplemented by application software using the PBX as a network telephonyservices provider.

In one embodiment, the telephony server 134 includes a trunk interfacethat utilizes conventional telephony trunk transmission supervision andsignaling protocols required to interface with the outside trunkcircuits from the PSTN 102. The trunk lines carry various types oftelephony signals such as transmission supervision and signaling, audio,fax, or modem data to provide plain old telephone service (POTS). Inaddition, the trunk lines may carry other communication formats such T1,ISDN or fiber service to provide telephony or multi-channel data images,video, text or audio.

The telephony server 134 includes hardware and software components tointerface with the LAN 132 of the contact center 100. In one embodiment,the LAN 132 may utilize IP telephony, which integrates audio streamcontrol with legacy telephony functions and may be supported through theH.323 protocol. H.323 is an International Telecommunication Union (ITU)telecommunications protocol that defines a standard for providing voiceservices over data networks. H.323 permits users to make point-to-pointaudio phone calls over a local area network. IP telephony systems can beintegrated with the public telephone system through an IP/PBX-PSTNgateway, thereby allowing a user to place telephone calls from anenabled computer. For example, a call from an IP telephony client withinthe contact center 100 to a conventional telephone outside of thecontact center would be routed via the LAN132 to the IP/PBX-PSTNgateway. The IP/PBX-PSTN gateway would then translate the H.323 protocolto conventional telephone protocol and route the call over the PSTN 102to its destination. Conversely, an incoming call from a customer overthe PSTN 102 may be routed to the IP/PBX-PSTN gateway, which translatesthe conventional telephone protocol to H.323 protocol so that it may berouted to a VoIP-enable phone or computer within the contact center 100.

The contact center 100 is further communicatively coupled to theInternet 104 via hardware and software components within the LAN 132.One of ordinary skill in the art would recognize that the LAN 132 andthe connections between the contact center 100 and external networkssuch as the PSTN 102 and the Internet 104 as illustrated by FIG. 2 havebeen simplified for the sake of clarity and the contact center mayinclude various additional and/or different software and hardwarenetworking components such as routers, switches, gateways, networkbridges, hubs, and legacy telephony equipment. It should be understoodthat the entire arrangement between customer and agent at the analyticscenter can be conducted via VoIP, or VoIP connected to traditionaltelephony, all in accordance with various aspects of the presentdisclosure. In various embodiments, the customer interactions that occurat the contact center 100 include video interactions with a voicecomponent including, but not limited to video conferences, Skype®sessions, and FaceTime® calls.

As shown in FIG. 2, the contact center 100 includes a plurality of agentworkstations 140 that enable agents employed by the contact center 100to engage in customer interactions over a plurality of communicationchannels. In one embodiment, each agent workstation 140 may include atleast a telephone and a computer workstation. In other embodiments, eachagent workstation 140 may include a computer workstation that providesboth computing and telephony functionality. Through the workstations140, the agents may engage in telephone conversations with the customer,respond to email inquiries, receive faxes, engage in instant messageconversations, respond to website-based inquires, video chat with acustomer, and otherwise participate in various customer interactionsessions across one or more channels. Further, in some embodiments, theagent workstations 140 may be remotely located from the contact center100, for example, in another city, state, or country. Alternatively, insome embodiments, an agent may be a software-based applicationconfigured to interact in some manner with a customer. An exemplarysoftware-based application as an agent is an online chat programdesigned to interpret customer inquiries and respond with pre-programmedanswers.

The contact center 100 further includes a contact center control system142 that is generally configured to provide recording, voice analysis,behavioral analysis, storage, and other processing functionality to thecontact center. In the illustrated embodiment, the contact centercontrol system 142 is an information handling system such as a computer,server, workstation, mainframe computer, or other suitable computingdevice. In other embodiments, the control system 142 may be a pluralityof communicatively coupled computing devices coordinated to provide theabove functionality for the contact center 100. The control system 142includes a processor 144 that is communicatively coupled to a systemmemory 146, a mass storage device 148, and a communication module 150.The processor 144 can be any custom made or commercially availableprocessor, a central processing unit (CPU), an auxiliary processor amongseveral processors associated with the control system 142, asemiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a collection of communicatively coupledprocessors, or any device for executing software instructions. Thesystem memory 146 provides the processor 144 with non-transitory,computer-readable storage to facilitate execution of computerinstructions by the processor. Examples of system memory may includerandom access memory (RAM) devices such as dynamic RAM (DRAM),synchronous DRAM (SDRAM), solid state memory devices, and/or a varietyof other memory devices known in the art. Computer programs,instructions, and data may be stored on the mass storage device 148.Examples of mass storage devices may include hard discs, optical disks,magneto-optical discs, solid-state storage devices, tape drives, CD-ROMdrives, and/or a variety other mass storage devices known in the art.Further, the mass storage device may be implemented across one or morenetwork-based storage systems, such as a storage area network (SAN). Thecommunication module 150 is operable to receive and transmit contactcenter-related data between local and remote networked systems andcommunicate information such as customer interaction recordings betweenthe other components coupled to the LAN 132. Examples of communicationmodules may include Ethernet cards, 802.11 WiFi devices, cellular dataradios, and/or other suitable devices known in the art. The contactcenter control system 142 may further include any number of additionalcomponents, which are omitted for simplicity, such as input and/oroutput (I/O) devices (or peripherals), buses, dedicated graphicscontrollers, storage controllers, buffers (caches), and drivers.Further, functionality described in association with the control system142 may be implemented in software (e.g., computer instructions),hardware (e.g., discrete logic circuits, application specific integratedcircuit (ASIC) gates, programmable gate arrays, field programmable gatearrays (FPGAs), etc.), or a combination of hardware and software.

According to one aspect of the present disclosure, the contact centercontrol system 142 is configured to record, collect, and analyzecustomer voice and other structured and unstructured data, and othertools may be used in association therewith to increase efficiency andefficacy of the contact center. As an aspect of this, the control system142 is operable to record unstructured interactions between customersand agents occurring over different communication channels includingwithout limitation telephone conversations, email exchanges, websitepostings, social media communications, smartphone application (i.e.,app) communications, fax messages, instant message conversations, etc.For example, the control system 142 may include a hardware orsoftware-based recording server to capture the audio of a standard orVoIP telephone connection established between an agent workstation 140and an outside customer telephone system. Further, the audio from anunstructured telephone call may be transcribed manually or automaticallyand stored in association with the original audio. In one embodiment,multiple communication channels (i.e., multi-channel) may be usedaccording to the invention, either in real-time to collect information,for evaluation, or both. For example, control system 142 can receive,evaluate, and store telephone calls, emails, and fax messages. Thus,multi-channel can refer to multiple channels of interaction data, oranalysis using two or more channels.

In addition to unstructured interaction data such as interactiontranscriptions, the control system 142 is configured to capturedstructured data related to customers, agents, and their interactions.For example, in one embodiment, a “cradle-to-grave” recording may beused to record all information related to a particular call or contactfrom the time the contact enters the contact center to the later of: thecustomer terminating contact or the agent completing the transaction.All or a portion of the interactions during the call or other contactmay be recorded, including interaction with an interactive voiceresponse (IVR) system, time spent on hold, data keyed through thecaller's key pad, conversations with the agent, and screens displayed bythe agent at his/her station during the transaction. Additionally,structured data associated with interactions with specific customers maybe collected and associated with each customer, including withoutlimitation the number and length of contacts placed to the contactcenter, contact origination information, reasons for interactions,outcome of interactions, average hold time, agent actions duringinteractions with customer, manager escalations during customer contact,types of social media interactions, number of distress events duringinteractions, survey results, and other interaction information. Inaddition to collecting interaction data associated with a customer, thecontrol system 142 is also operable to collect biographical profileinformation specific to a customer including without limitation customerphone number or email address, account/policy numbers, address,employment status, income, gender, race, age, education, nationality,ethnicity, marital status, credit score, customer “value” data (i.e.,customer tenure, money spent as customer, etc.), personality type (e.g.,as determined by past interactions), and other relevant customeridentification and biological information. The control system 142 mayalso collect agent-specific unstructured and structured data includingwithout limitation agent personality type, gender, language skills,performance data (e.g., customer retention rate, etc.), tenure andsalary data, training level, average hold time during interactions,manager escalations, agent workstation utilization, and any other agentdata relevant to contact center performance. Additionally, one ofordinary skill in the art would recognize that the types of datacollected by the contact center control system 142 that are identifiedabove are simply examples and additional and/or different interactiondata, customer data, agent data, and telephony data may be collected andprocessed by the control system 142.

The control system 142 may store recorded and collected interaction datain a database 152, including customer data and agent data. In certainembodiments, agent data, such as agent scores for dealing withcustomers, are updated daily. The database 152 may be any type ofreliable storage solution such as a RAID-based storage server, an arrayof hard disks, a storage area network of interconnected storage devices,an array of tape drives, or some other scalable storage solution locatedeither within the contact center or remotely located (i.e., in thecloud). Further, in other embodiments, the contact center control system142 may have access not only to data collected within the contact center100 but also data made available by external sources such as a thirdparty database 154. In certain embodiments, the control system 142 mayquery the third party database for customer data such as credit reports,past transaction data, and other structured and unstructured data.

In some embodiments, an analytics system 160 may also perform some orall of the functionality ascribed to the contact center control system142 above. For instance, the analytics system 160 may record telephoneand internet-based interactions, as well as perform behavioral analyses,predict customer personalities or customer profiles, retrievepre-existing customer profiles, and perform other contact center-relatedcomputing tasks, as well as combinations thereof. The analytics system160 may be integrated into the contact center control system 142 as ahardware or software module and share its computing resources 144, 146,148, and 150, or it may be a separate computing system housed, forexample, in the analytics center 120 shown in FIG. 1. In the lattercase, the analytics system 160 includes its own processor andnon-transitory computer-readable storage medium (e.g., system memory,hard drive, etc.) on which to store predictive analytics software andother software instructions.

The interaction data collected from one channel or multi-channels in thecontext of the control center 100 may be subject to a linguistic-basedpsychological behavioral model to assess the personality of customersand agents associated with the interactions. For example, such abehavioral model may be applied to the transcription of a telephone callbetween a customer and agent to gain insight into why a specific outcomeresulted from the interaction.

In one embodiment, a voice analysis module in contact center controlsystem 142 mines interaction data for behavioral signifiers associatedwith a linguistic-based psychological behavioral model. In particular,the voice analysis module searches for and identifies text-basedkeywords (i.e., behavioral signifiers) relevant to a predeterminedpsychological behavioral model. In a preferred embodiment,multi-channels are mined for such behavioral signifiers.

It is well known that certain psychological behavioral models have beendeveloped as tools, and any such behavioral model available to those ofordinary skill in the art will be suitable for use in connection withthe disclosure. These models are used to attempt to evaluate andunderstand how and/or why one person or a group of people interacts withanother person or group of people. One example is the Big Five inventorymodel (©2000) by UC Berkeley psychologist Oliver D. John, Ph. D. Anotheris the Process Communication Model™ developed by Dr. Taibi Kahler.Exemplary personality types, which will vary from model to model and canbe selected as desired for a given application or across allapplications, might include, for example: Thoughts, Opinions, Reactions,Emotions. These models generally presuppose that all people fallprimarily into one of the enumerated basic personality types. In somecases, the models categorize each person as one of these four types (orsome other number of personality types), all people have parts of eachof the types within them. Each of the types may learn differently, maybe motivated differently, may communicate differently, and may have adifferent sequence of negative behaviors in which they engage undercertain circumstances, e.g., when they are in distress. Importantly,each personality type may respond positively or negatively tocommunications that include tones or messages commonly associated withanother of the personality types. Thus, an understanding of a user'spersonality type typically offers guidance as to how the user will reactor respond to different situations.

Linguistic algorithms can be applied to the text of the communicationand yield a personality type. A linguistic algorithm(s) is typicallycreated by linguistic analysts and such algorithm(s) are typicallytrained using previously analyzed customer-agent communications. In oneembodiment, the analyst(s) can review communications and manually labelkeywords or terms that are relevant to an identified personality type.The computer-implemented algorithm is trained to check for thosekeywords and the number of times they are used in the communications. Amore sophisticated algorithm may be used that additionally checks foruse of the keywords in context. One master algorithm containing manyspecific algorithms may also be used.

In addition to determining personality type from the interaction data,the control system 142 may also or alternatively apply distress analysistechniques to the interaction data to detect distress events. Forexample, when applied to a telephone-based interaction session,linguistic-based distress analysis may be conducted on both a textualtranslation of voice data and an audio file containing voice data.Accordingly, linguistic-based analytic tools as well as non-linguisticanalytic tools may be applied to the audio file. In particular, thecontrol system 142 may apply spectral analysis to the audio file voicedata while applying a human speech/linguistic analytical tool to thetext file. Linguistic-based analysis and computer-implemented algorithmsfor identifying distress can be applied to the textual translation ofthe communication. Resultant distress data may be stored in the database152 or elsewhere for subsequent analysis of the communication. Distressevent data and other linguistic-based analytic data may be consideredbehavioral assessment data in some instances. Further, in otherembodiments, the control system 142 may be operable to apply voiceprinting techniques to the unstructured audio from various customerinteractions. For example, a recorded sample may be utilized toidentify, or facilitate identification of, a customer in the event thecustomer did not supply any identifying information.

An exemplary method 300 of analyzing a customer-agent communicationaccording to the disclosure will now be described with respect to FIG.3. At step 302, the contact center control system 142 receives acommunication between an agent and a customer. In various embodiments,the communication is received from the contact center 100, but in otherembodiments, the communication may be received from a user device. Thecommunication may be received in any form of electronic communication,including text based (email, text, web interaction) or recorded verbal(telephonic) responses or video based responses. The communication maybe stored for later use.

At step 304, the contact center control system 142 receives and recordsa mono recording of the communication that includes both the customerand agent voices. By “mono” is meant particularly that separate channelsare not used to record the agent and the customer sides of thecommunication. A mono recording is a recording that is done on onesingle channel. In a mono recording, a single audio stream can containthe two sides of the call or interaction.

At step 306, in an optional but preferred embodiment, the control system142 separately but concurrently records only the agent's voice in thecommunication. In various embodiments, the agent recording is also amono recording.

At step 308, the control system 142 aligns the mono recording and theagent recording so that they are synchronized or matched. For example,the mono recording and agent recording can be time-synched, matched bysound analysis, or both. Another exemplary method to align differentrecordings is the cross-correlation of audio segments. This approachcan, for example, take two audio files at a given point in time andcompare the amplitude of the audio samples and calculate the error rateof subtracting the two samples, or one sample from the other. If thefiles were both agent only, the “error rate” would be zero since the twosignals would perfectly match at that point in time. Thus, the processof this example can iteratively compare the two files until a minimalerror rate exists across time. In the specific case of comparing themono file with the agent file, the error rates will not be zero acrossthe whole audio file (since the customer is mixed in at points in time),but the minimal error rate at points or along the audio timeline willstill result in maximum alignment of the two files. Thus, this is a thepreferred method for alignment of two audio files.

At step 310, the control system 142 subtracts or removes the agent sideor agent voice of the communication from the mono recording so that onlythe customer side or customer voice of the communication remains on themono recording. In another embodiment, the agent voice data is removedfrom the mono recording by recording, copying, or otherwise transferringonly the customer voice data or customer side of the communication to aseparated recording. Generally, the separated recording is the monorecording without the agent voice data.

In certain embodiments, the agent's side is removed from the monorecording by sound frequency analysis. For example, the agent recordingcan be analyzed to determine the sound frequencies present in therecording. The agent's voice may have a frequency (or range offrequencies) different from the customer's voice so that the agent'svoice can be identified and removed by removing certain frequencies fromthe mono recording or certain time segments where those frequenciesexist (or where only those frequencies exist, which would potentiallyretain segments where both agent and customer voices are present). Insome embodiments, other characteristics of the agent voice such aspitch, tone, volume, or vibration may be determined from the agentrecording and used to remove the agent voice from the mono recording. Inone exemplary embodiment, the approached used is essentially a speakerseparation technique, with the speaker model being “seeded” by a voiceprint generated from the agent only audio (e.g., agent acoustic model).Thus, in this approach, an acoustic model of the agent's voice isgenerated from the agent-only file or a separate agent-only recording.One or more segments of the mono file are then analyzed by comparing thesegment to the acoustic model, and finding the segments that closelymatch the acoustic model generated as well as segments that are distant,or different from, the generated acoustic model of the agent. The twotypes of segments are characterized as agent or customer accordingly(based on how near to, or far from, the model, respectively). Thesegments of the audio that are typed as agent (or score nearest to agentacoustic model based on a threshold) can then be sliced out, orotherwise eliminated, and the remaining file is primarily thecustomer-only recording. This separated file can be further analyzed forcustomer personality trait or other customer analysis.

At step 312, the control system 142 converts the customer side of themono recording (or the separated recording, not shown) to text. At step314, the control system 142 applies at least one linguistic-basedpsychological behavioral model or computer-implemented algorithm to thetext to determine personality type of the customer. The algorithm, forexample, looks for specific terms, keywords and phrases (i.e., groups ofkeywords) that indicate a specific personality type and the density ofthose terms in the text.

In various embodiments, these terms, phrases, or keywords are stored ina library or libraries that are accessed by the control system 142. Thelibrary may separate the keywords, terms, and phrases into differentpersonality types. Keywords are the words previously determined toindicate the specific characteristic in the input. Each keyword may haverespective aliases, which are essentially synonyms of keywords. Synonymsof the keywords may be identified and also stored in the library. Thealiases are typically treated as interchangeable with the keywords froma scoring perspective, but in one embodiment aliases can be treated asnot interchangeable if specific words, terms, or phrases are expected tobe used. Also, due to the flexibility of the methods described herein,additional words, terms, and/or phrases may be added to the library atany time, such as based on additional input, external analysis ofterminology, or both. For example, when it becomes apparent that anotherword is used frequently and is just as effective as the associatedkeyword, the library may be updated to include this word as anacceptable alias. In other embodiments, contextual weighting based onkeywords used in context with certain related words may be used, to helpdetermine personality type when a keyword is used by multiplepersonality types.

The control system 142, in one embodiment, uses one or morecomputer-implemented linguistic algorithms that are configured to detectkeywords, terms, and phrases in the text related to personality type,and the text can be scored based on the number of word hits (i.e., basedon keywords, terms, phrases, etc.). When a score is assigned to the textit can be associated with and identify the personality type of thecustomer. For example, reactions-type personalities use emotional words,opinions-types use opinion words, emotions-types use reflection words,and reactions-types use reaction words.

The personality type of the customer can then be used to improve thequality of future customer interactions with agents and ultimatelycustomer relationships, beginning immediately on that interaction forreal-time analysis of personality-type and other input discussed herein,or later on future calls by storing that information in association witha customer identifier. For example, the personality type of the customermay be used to distribute customer tasks or communications to the bestavailable agent on duty based on personality type, provide certain wordsand phrases to an agent to use with the customer, determine servicesneeds by the customer, predict customer behavior, and generateactionable agent tasks for the customer.

In various embodiments, the agent recording is also converted to textand the agent's personality type is determined. The personality type ofthe agent can be used to determine the best training methods for theagent, customers that should be matched with the agent, and measureagent performance.

Another exemplary method 400 of analyzing a customer-agent communicationaccording to the disclosure will now be described with respect to FIG.4. Steps 402-406 are identical to steps 302-306 described above.

At step 408, the control system 142 converts the mono recording of thecustomer and agent voices to text. Each word includes a time notation ofwhen the word was spoken. For example, a word may have a time rangeassociated with it, such as an hour and minute range, a minute andsecond range, or an hour, minute, and second range, depending on theduration of an interaction and the frequency with which the differentparties to the interaction talk.

At step 410, the control system 142 converts the agent recording totext. Each word also includes a time notation of when the word wasspoken. When referring to the “word”, it should be understood thatmultiple words can be spoken at a time, so reference to a word beingspoken can include a phrase, sentence, paragraph, a speech, etc.

At step 412, based on the time notation, the control system 142subtracts text of the agent recording from the text of the monorecording, leaving the customer's word(s).

At step 414, the control system 142 applies at least onelinguistic-based psychological behavioral model or computer-implementedalgorithm to the customer text of the communication to determinepersonality type of the customer. This step is analogous to step 314above.

In another exemplary method, a customer-agent communication is analyzedusing only the mono recording of the agent and customer voices. In thisembodiment, agent and customer voices are recorded during thecommunication, but the agent tracks or notes the times when he or she isspeaking (e.g., by pushing an on/off button to open a microphone and/orto designate when he or she is talking) so that these portions can bemore easily tracked and subtracted or removed from the mono recording(or to generate a separated recording with just the customer voice datamore easily). For example, the mono recording can be converted to text,and the on/off portions can be marked in the text and removed.

Referring now to FIG. 5, illustrated is a block diagram of a system 500suitable for implementing embodiments of the present disclosure,including contact center control system 142 and analytics system 160.System 500, such as part a computer and/or a network server, includes abus 502 or other communication mechanism for communicating information,which interconnects subsystems and components, including one or more ofa processing component 504 (e.g., processor, micro-controller, digitalsignal processor (DSP), etc.), a system memory component 506 (e.g.,RAM), a static storage component 508 (e.g., ROM), a network interfacecomponent 512, a display component 514 (or alternatively, an interfaceto an external display), an input component 516 (e.g., keypad orkeyboard), and a cursor control component 518 (e.g., a mouse pad).

In accordance with embodiments of the present disclosure, system 500performs specific operations by processor 504 executing one or moresequences of one or more instructions contained in system memorycomponent 506. Such instructions may be read into system memorycomponent 506 from another computer readable medium, such as staticstorage component 508. These may include instructions to receive acustomer-agent communication, separate agent and customer voice data,convert the customer voice data to text, determine personality type ofthe customer and/or agent, etc. In other embodiments, hard-wiredcircuitry may be used in place of or in combination with softwareinstructions for implementation of one or more embodiments of thedisclosure.

Logic may be encoded in a computer readable medium, which may refer toany medium that participates in providing instructions to processor 504for execution. Such a medium may take many forms, including but notlimited to, non-volatile media, volatile media, and transmission media.In various implementations, volatile media includes dynamic memory, suchas system memory component 506, and transmission media includes coaxialcables, copper wire, and fiber optics, including wires that comprise bus502. Memory may be used to store visual representations of the differentoptions for searching or auto-synchronizing. In one example,transmission media may take the form of acoustic or light waves, such asthose generated during radio wave and infrared data communications. Somecommon forms of computer readable media include, for example, RAM, PROM,EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier wave, orany other medium from which a computer is adapted to read.

In various embodiments of the disclosure, execution of instructionsequences to practice the disclosure may be performed by system 500. Invarious other embodiments, a plurality of systems 500 coupled bycommunication link 520 (e.g., networks 102 or 104 of FIG. 1, LAN, WLAN,PTSN, or various other wired or wireless networks) may performinstruction sequences to practice the disclosure in coordination withone another. Computer system 500 may transmit and receive messages,data, information and instructions, including one or more programs(i.e., application code) through communication link 520 andcommunication interface 512. Received program code may be executed byprocessor 504 as received and/or stored in disk drive component 510 orsome other non-volatile storage component for execution.

In view of the present disclosure, it will be appreciated that variousmethods, apparatuses, computer readable media, and systems have beendescribed according to one or more embodiments for analyzing acustomer-agent communication.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the spirit of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components, andvice-versa.

Software in accordance with the present disclosure, such as program codeand/or data, may be stored on one or more computer readable mediums. Itis also contemplated that software identified herein may be implementedusing one or more general purpose or specific purpose computers and/orcomputer systems, networked and/or otherwise. Where applicable, theordering of various steps described herein may be changed, combined intocomposite steps, and/or separated into sub-steps to provide featuresdescribed herein.

The various features and steps described herein may be implemented assystems comprising one or more memories storing various informationdescribed herein and one or more processors coupled to the one or morememories and a network, wherein the one or more processors are operableto perform steps as described herein, as non-transitory machine-readablemedium comprising a plurality of machine-readable instructions which,when executed by one or more processors, are adapted to cause the one ormore processors to perform a method comprising steps described herein,and methods performed by one or more devices, such as a hardwareprocessor, user device, server, and other devices described herein.

The foregoing outlines features of several embodiments so that a personof ordinary skill in the art may better understand the aspects of thepresent disclosure. Such features may be replaced by any one of numerousequivalent alternatives, only some of which are disclosed herein. One ofordinary skill in the art should appreciate that they may readily usethe present disclosure as a basis for designing or modifying otherprocesses and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein. Oneof ordinary skill in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions andalterations herein without departing from the spirit and scope of thepresent disclosure.

The Abstract at the end of this disclosure is provided to allow a quickdetermination of the nature of the technical disclosure. It is submittedwith the understanding that it will not be used to interpret or limitthe scope or meaning of the claims.

What is claimed is:
 1. A system for interpreting customer inquirycommunications and responding, comprising: a node comprising a processorand a non-transitory computer readable medium operably coupled thereto,the non-transitory computer readable medium comprising a plurality ofinstructions stored in association therewith that are accessible to, andexecutable by, the processor, wherein the plurality of instructionsthat, when executed: record, using a microphone, a mono recording of acustomer inquiry communication received by a software agent comprising asoftware-based application, wherein the mono recording is unseparatedand includes customer voice data and audio data generated by thesoftware agent; separately and concurrently record the software agentaudio data in an agent recording; and subtract the software agent audiodata from the unseparated mono recording to provide a separatedrecording including only customer voice data.
 2. The system of claim 1,further comprising instructions that, when executed, determine apersonality type of the customer.
 3. The system of claim 1, whereininstructions that, when executed determine the personality type furthercomprise instructions that, when executed, retrieve the personality typefrom a customer record associated with that customer, or convert atleast the customer voice data to text and apply one or morecomputer-implemented linguistic algorithms to the text of the customervoice data to determine the personality type.
 4. The system of claim 1,further comprising a second microphone that is used to record the agentaudio data.
 5. The system of claim 1, wherein the customer inquirycommunication comprises one or more of an email, website posting, orinstant message.
 6. The system of claim 1, wherein the customer inquirycommunication occurs via a communication channel comprising one or moreof a telephone, video, VOIP, fax, email, or website.
 7. The system ofclaim 6, wherein the communication channel includes a web site and isbased on one or more of internet survey data, blog data, microblog data,discussion forum data, and chat data.
 8. The system of claim 1, whereinthe customer inquiry communication comprises an internet-basedinteraction session from a personal computing device.
 9. The system ofclaim 3, which further comprises instructions that, when executed,predict a customer personality type.
 10. A method for interpretingcustomer inquiry communications and responding, which comprises:recording, using a microphone, a mono recording of a customer inquirycommunication received by a software agent comprising a software-basedapplication, wherein the mono recording is unseparated and includescustomer voice data and audio data generated by the software agent;separately and concurrently recording the software agent audio data inan agent recording; and subtracting agent audio data from theunseparated mono recording to provide a separated recording includingonly customer voice data.
 11. The method of claim 10, which furthercomprises determining a personality type of the customer.
 12. The methodof claim 10, wherein determining the personality type further comprisesretrieving the personality type from a customer record associated withthat customer, or converting at least the customer voice data to textand applying one or more computer-implemented linguistic algorithms tothe text of the customer voice data to determine the personality type.13. The method of claim 10, which further comprises recording the agentaudio data with a second microphone.
 14. The method of claim 10, whereinthe customer inquiry communication is selected to comprise one or moreof an email, website posting, or instant message.
 15. The method ofclaim 10, wherein the customer inquiry communication is selected tooccur via a communication channel comprising one or more of a telephone,video, VOIP, fax, email, or website.
 16. The method of claim 15, whereinthe communication channel includes a website and is selected based onone or more of internet survey data, blog data, microblog data,discussion forum data, and chat data.
 17. The method of claim 10,wherein the customer inquiry communication comprises an internet-basedinteraction session from a personal computing device.
 18. The method ofclaim 12, which further comprises predicting a customer personalitytype.
 19. A non-transitory computer readable medium comprising aplurality of instructions, which in response to a computer system, causethe computer system to perform a method comprising: recording, using amicrophone, a mono recording of a customer inquiry communicationreceived by a software agent comprising a software-based application,wherein the mono recording is unseparated and includes customer voicedata and audio data generated by the software agent; separately andconcurrently recording the software agent audio data in an agentrecording; and subtracting agent audio data from the unseparated monorecording to provide a separated recording including only customer voicedata.
 20. The method of claim 19, which further comprises determining apersonality type of the customer.
 21. The method of claim 19, whereindetermining the personality type further comprises retrieving thepersonality type from a customer record associated with that customer,or converting at least the customer voice data to text and applying oneor more computer-implemented linguistic algorithms to the text of thecustomer voice data to determine the personality type.
 22. The method ofclaim 19, which further comprises recording the agent audio data with asecond microphone.
 23. The method of claim 19, wherein the customerinquiry communication is selected to comprise one or more of an email,website posting, or instant message.
 24. The method of claim 19, whereinthe customer inquiry communication is selected to occur via acommunication channel comprising one or more of a telephone, video,VOIP, fax, email, or website.
 25. The method of claim 24, wherein thecommunication channel includes a website and is selected based on one ormore of internet survey data, blog data, microblog data, discussionforum data, and chat data.
 26. The method of claim 19, wherein thecustomer inquiry communication comprises an internet-based interactionsession from a personal computing device.
 27. The method of claim 21,which further comprises predicting a customer personality type.